Jun 26, 2024 · InfoNCE, short for Information Noise-Contrastive Estimation, is a loss function commonly used in self-supervised learning, particularly in representation learning tasks.

InfoNCE, where NCE stands for Noise-Contrastive Estimation, is a type of contrastive loss function used for self-supervised learning.

May 31, 2021 · The InfoNCE loss in CPC (Contrastive Predictive Coding; van den Oord, et al. 2018), inspired by NCE, uses categorical cross-entropy loss to identify the positive sample amongst a set of unrelated noise samples.

Aug 19, 2023 · The NT-Xent loss encourages the model to pull embeddings of positive augmentation pairs closer together in the embedding space, while pushing embeddings of negative augmentation pairs farther...

InfoNCE loss is the improvement over the Contrastive Loss In InfoNCE, there is one anchor, one positive and multiple negative samples for each instance It converge faster than the Triplet Loss In short, it is the Cross Entropy of the positive ones

Sep 25, 2023 · We propose the Supervised InfoNCE REvisited (SINCERE) loss as a theoretically-justified supervised extension of InfoNCE that eliminates intra-class repulsion. Experiments show that SINCERE leads to better separation of embeddings from different classes and improves transfer learning classification accuracy.

PyTorch implementation of the InfoNCE loss from "Representation Learning with Contrastive Predictive Coding". In contrastive learning, we want to learn how to map high dimensional data to a lower dimensional embedding space.

We introduce AnInfoNCE, a generalized and identifiable contrastive loss assuming anisotropic positive pair distributions (Sec. 3.2). We propose a model for hard negative mining that is provably identifiable (Sec. 3.3) and also extend our main identifiability result to loss ensembles (Sec. 3.4).

InfoNCE, which stands for Noise-Contrastive Estimation, is a loss function utilized in self-supervised learning. This approach aims to train a model without any external labels or annotations but instead, leverages the inherent structure in the data to learn features that can be used in downstream tasks such as classification or clustering.

May 27, 2021 · InfoNCE loss is a widely used loss function for contrastive model training. It aims to estimate the mutual information between a pair of variables by discriminating between each positive pair and its associated K negative pairs.